Gestational Diabetes Mellitus - Case Study Example

Factors that Determine Gestational Diabetes Mellitus: A Case Control Study in a Tertiary Obstetric Hospital in Queensland Name Course Name and Code Instructor’s Name Date Background Gestational diabetes mellitus (GDM) is the recognition or onset of glucose intolerance during gestational period. GDM is linked to increased risk of morbidity and mortality during pregnancy (Feig et al., 2008, p. 230). Studies have indicated that women who are diagnosed with GDM are likely to develop diabetes in future (Shah, Retnakaran and Booth, 2008, p. 1668). It is recommended that where resources are available universal screening need to be undertaken and where resources are limited or where the incidence of GDM is low selective screening is recommended (Gunderson et al., 2010, p. 1132). Various factors have been documented to be responsible for GDM (Nanda et al. 2011, p. 139). Among these factors is glycosuria, obesity, age over 30 years, family history of diabetes, past history of glucose intolerance or GDM, polyhydramnios, recurrent moniliasis, recurrent urinary tract infections (UTI), preeclampsia in multipara, delivery of a large infant (>4kg), treatment of infertility, belonging to a high risk ethnic group and previous adverse pregnancy outcome. It is recommended that the screening of GDM be performed at 26-28 weeks of gestation period (Fadl et al., 2010, p. 437). Studies have indicated that about 10% of pregnancies are complicated by GDM with its incidence rising rapidly (Kim and Ferrara, 2010, p. 19). In Australia, evidence linking these factors to GDM is scarce. Studies previously carried out aimed at documenting statistics of the condition. Impact of gestational diabetes mellitus (GDM) The Australian Diabetes in Pregnancy Society (ADIPS) is in consensus that there are no good quality randomized controlled clinical trials in the area of GDM. The existing guidelines for management and diagnosis of GDM are based on what is a reasonable consensus (Orshan, 2007, p. 61). The implication of factors such as glycosuria, age, obesity, family history of diabetes, past history of GDM or glucose intolerance, previous adverse pregnancy outcome and ethnicity as causative of GDM are based on studies carried out in other countries and not in Australia. The first GDM statistics were released in 2008 and covered 2005 to 2006. The report used data from the national diabetes services scheme to determine GDM cases among Australian women of child bearing age. It was reported that GDM was diagnosed in 4.6% of confinements among women aged 15 to 49 years. It was reported that the incidence of confinements with GDM increased by over 20% between 2000-01 and 2005-06 among women aged 15 to 49 years. This is an indication that the incidence of GDM is on the increase among Australian women. It was also reported that women born overseas were twice likely to develop GDM than those born in Australia (Fadl et al., 2010, p. 440). The report also indicated that the risk GDM increases with age from 1% among 15-19 year old women to 13% among women aged 44 to 49 years. The report also showed that 30% of GDM was accounted for by women aged between 30 and 34 years in 2005-06 (Xiang et al., 2010, p. 2626). The report also indicated that aboriginal and Torres Strait Islander had 1.5 higher incident rates than other Australian women (Kim and Ferrara, 2010, p. 31). The table below provides a summary of the GDM statistics in Australia in 2005 to 2006. GDM statistics in Australia in 2005 to 2006 GDM among women aged 15 to 49 years 4.6% Increase of incidence of GDM between 2000-01 and 2005-06 20% Risk of GDM among women aged 15-19 1% Risk of GDM among women aged 44-49 13% Incidence of GDM among women aged 30 to 34 years 30% Another study carried out in Australian tertiary obstetric hospital in Monash indicated that increased age, body mass index (BMI) and previous GDM were the most significant factors for GDM prediction (Nanda et al. 2011, p. 139). However, the study was limited by the fact that the glucose challenge test (GCT) method used for screening in the study could fail to identify some women with GDM and lack of documentation of other predisposing factors such as glycosuria. Some studies have indicated that untreated GDM results in increased perinatal mortality rates (Xiang et al., 2010, p. 2627). This implies that the increasing rates of GDM in Australia are likely to result in increased perinatal mortality rates if not diagnosed early and treated (Ekoe et al., 2008, p. 59). Identifying risk factors in a well case controlled study is one of the first steps in helping in diagnosis of GDM in resource limited scenarios. Target group This control study will be carried out from January, 2012 to December, 2012 at The Townsville Hospital (TTH). The Townsville Hospital (TTH) is a tertiary referral hospital located in the suburb of Douglas, Townsville. The target group for the study will be pregnant women visiting the hospital. The study will seek to find out the pregnant women who develop GDM during their pregnancy and ascertain predisposing factors amongst these women. Intervention In this study pregnant women who develop GDM will be compared to pregnant women who do not develop GDM during the study period. Pregnant women visiting the hospital will be monitored with glucose challenge test (GCT) at 24 to 28 weeks and 32 to 34 weeks or whenever any risk factor develops during pregnancy. In case the GCT is found to be positive, a gestational diabetes status will be confirmed with the Oral Glucose Tolerance Test (OGTT). Pregnancy women with high risk of developing GDM will be screened with OGTT on their first antenatal visit. Any of these patients who develop glucose intolerance will be included in the study group (Shah, Retnakaran and Booth, 2008, p. 1668). The control group will include the next pregnant woman of the same age who will be having a normal GCT at 24 to 28 weeks, followed by a normal OGTT with 100 gm of glucose (age matched control). The prenatal patients will be given a 50 gm GCT and if the plasma glucose value after one hour will exceed 130 mg/dl, a 100 gm OGTT will be performed after overnight fasting. Plasma samples will then be drawn at an interval of one, two and three hours after administration of glucose (Feig et al., 2008, p. 233). For the purpose of this study, the OGTT results will be interpreted by the use of Australian Diabetes in Pregnancy Society data group values. Thus abnormal values will be defined as follows: FBS > 105 mg%, one hour > 190 mg%, two hours > 165 mg% and three hours > 145 mg%. Any patient who will have two or more abnormal values will be classified as a gestational diabetic. Any women who will have been diagnosed with diabetes prior to pregnancy will be excluded from the study (Fadl et al., 2010, p. 437). Purpose of the study and expected outcomes The statement which is used to identify issues to be studied in a research is referred to as the research purpose. Research purpose is the situation which compels the research to feel apprehensive of carrying out the study (Xiang et al., 2010, p. 2629). In this case the compelling issue for this research is that guidelines for screening GDM are based on studies carried out Australia and thus some women suffering from GDM during might be left out using the current method and thus there is need for provision of empirical data carried out in Australian setting to support the guideline. For one to be able to formulate a research purpose which is strong he or she should be familiar with the field of research. In addition, he or she should be familiar with vital research questions in his or her area of study (Nanda et al. 2011, p. 137). The researcher should also be able to identify fields which require more research to be able to formulate a research question (Fadl et al., 2010, p. 438). The research question should be aimed at bringing more understanding to the field of study or filling a knowledge gap. This study will be able to provide empirical data to be based on when developing guidelines for diagnosis of GDM. The researchers also need to know what has already been done in the field of study and what needs to be improved to be able to formulate a research question (Kim and Ferrara, 2010, p. 119). The relevance of the study also is vital in the formulation of the research question. This study is relevant since it will enable proper methods tpo be used for diagnosis of GDM among pregnant women in Australia. Finally, the significance of the knowledge gained in the proposed study should be considered to be able to formulate a research question. Thus, the purpose of this study is to determine factors predisposing pregnancy women in Australia to gestational diabetes mellitus (GDM). It is expected that the study will be able to elucidate the predisposing factor to GDM among Australian pregnant women. It is also expected that the results of the study will form a key component for future development of guidelines for diagnosis of GDM in Australia. Study design A hospital based case control study will be used in this study. This design is instrumental in the identification of factors which contribute to a medical condition and in this case the design will help in elucidating factors which predispose pregnant women in Australia to gestational diabetes mellitus (Xiang et al., 2010, p. 2629). The design allows a comparison to be made between the subjects who have the condition with the patients who lack the condition but are otherwise similar. Thus in this study pregnant women who develop GDM will be compared to pregnant women who do not develop GDM during the study period. This method is advantageous because it is relatively inexpensive and is the frequently used epidemiological study which can be carried out by limited number of individual researchers or small teams in single facilities in a way which more structured experimental studies cannot be carried out (Xiang et al., 2010, p. 2627). Thus, this study will employ this design to carry out a case control study in The Townsville Hospital (TTH). This design method has been instrumental in providing various diseases to causative factors including provision of proof for a linkage between tobacco smoking and lung cancer (Nanda et al. 2011, p. 137). Case control studies are also advantageous because one can carry out the study in a short period of time. Thus the design will be employed in this study to allow it to come to completion within a period of one year (Feig et al., 2008, p. 231). The design is also hailed for its greater statistical power in various situations (Kim and Ferrara, 2010, p. 41). However, the method has some drawbacks such as being observational in nature and hence being unable to provide the same evidence as randomized controlled trials. It is also not easy to obtain reliable information about an individual’s exposure status over time (Shah, Retnakaran and Booth, 2008, p. 1669). In spite the shortcomings of case control design, this study will employ a case control design because many high quality and reliable case control studies have been undertaken and provided informative and useful results (Xiang et al., 2010, p. 2627). Sample selection Recruitment in research involves a set of practices and activities used for identifying sufficient numbers and quality of persons fit for a given study. A sample of any quantitative research should be a representation of the whole population (Fadl et al., 2010, p. 439). This study will use purposive sampling. Purposive sampling implies that the researcher will be seeking for pregnant women visiting The Townsville Hospital (TTH) (Xiang et al., 2010, p. 2627). This is because the people of interest to this study are pregnant women visiting this hospital and not any other patient (Bowers et al., 2011, p. 1559). The participants will be allocated to either the control or the intervention group based on whether they have GDM or not. Data collection methods The following risk factors will be assessed and recorded during the study: socio demographic characteristics, obstetric history, menstrual history, and history of infertility, recurrent UTI, family history of diabetes in first degree relatives, moniliasis and premature labour pains. Obstetric history will include previous pregnancy losses, congenital anomalies, macrosomia, preeclampsia and polyhydramnios (Nanda et al. 2011, p. 137). During examination a note will be taken about the weight and height of the patient (Xiang et al., 2010, p. 2629). A specific question in relation to pre-pregnancy weight will be asked and if such information will not be available the weight recorded in the first prenatal visit in early pregnancy will be noted and be used for calculating the pre-pregnancy BMI (kg/m2). A comprehensive examination of the patient will be carried out to establish things such as heart rate and blood pressure (Shah, Retnakaran and Booth, 2008, p. 1668). The symphysio-fundal height will be measured and this will be confirmed using ultrasound to note any effective foetal weight greater than 4 kg and amniotic fluid index greater than 25. Data analysis The data will be analyzed using SPSS version 15. A t-Test will be performed to compare various variables (Shah, Retnakaran and Booth, 2008, p. 1669). The variables in the study will include socio demographic characteristics, obstetric history, menstrual history, and history of infertility, recurrent UTI, family history of diabetes in first degree relatives, moniliasis and premature labour pains. Obstetric history will include previous pregnancy losses, congenital anomalies, macrosomia, preeclampsia and polyhydramnios. A Chi square test and Odd’s ratio (crude and adjusted) will be calculated. A P value of less than 0.05 will be considered to be statistically significant (Poretsky, 2010, p. 29). A multiple logistic regression analysis will be used to assess the independence of various variables under consideration because GDM is a mulfactorial condition. The logistic regression analysis will be used to adjust Odds ratios and calculate 95% confidence intervals. Research ethics This research will observe and adhere to the ethical standards and issues with regard to: voluntary nature of participation and the right to withdraw by individual employees from the process (Nnakwe, 2009, p. 59). The research shall maintain the confidentiality of data provided by individuals or identifiable participants and their anonymity (Feig et al., 2008, p. 231). Data collected and information will be exclusively used for the purpose of this academic work (Nanda et al. 2011, p. 138). A consent form will be developed and be approved by the hospitals ethics department (Escott-Stump, 2008, p. 102). The participants will be expected to sign the consent form prior to participating in the study. To ensure anonymity and confidentiality of data to be obtained will go directly to the database (Fadl et al., 2010, p. 440). Thus ethical issues will be adequately covered in the research (Xiang et al., 2010, p. 2627). The study will seek approval of the study from Queensland Diabetes ethics department. Intervention for GDM Most screen strategies for GDM in Australia are based on the presence or absence of recognized risk factors for GDM. However, the identification of these factors is based on studies carried out in other areas countries outside Australia (Gillman et al., 2010, p. 967). The factors currently used as basis for screening GDM in Australia include Glycosuria, Age greater than 30 years, Obesity, A family history of Type 2 diabetes, Past history of GDM or glucose intolerance, Previous adverse pregnancy outcome, and Belonging to high-risk ethnic group (e.g. Australian Indigenous, Polynesian, South Asian/Indian) (Poretsky, 2010, p. 51). However, other factors exists which have been implicated for predisposing pregnant women to GDM. These include a history of increasing maternal body mass index (BMI), a woman’s own low birth weight, polyhydramios or a large for gestational age foetus in the current pregnancy (Fadl et al., 2010, p. 438). Most women who have no predisposing factors are excluded from being diagnosed for GDM. Even though universal screening is recommended in Australia by Australasian Diabetes in Pregnancy Society (ADIPS) it however advises that where resources are limited, or in areas of low incidence, selective screening based on risk factors may be appropriate (Ekoe et al., 2008, p. 43). This study will be instrumental in providing empirical data for designing a guideline for screening GDM among pregnant women in Australia. This will ensure that management and treatment of gestational diabetes does not leave out any patient. It is known that the treatment and management of GDM is a team effort that involves the woman with GDM, doctor, family and specialists, credentialed diabetes educator and dieticians (Shah, Retnakaran and Booth, 2008, p. 1668). Three basic components are said to be important for effective management of gestational diabetes: monitoring blood glucose levels, adopting a healthy eating pattern and physical activity. Thus by basing screening of GDM on empirical data the management of GDM in Australia will be enhanced. References Bowers, K., Yeung, E., Williams, M., Qi, L., Tobias, D., Hu, F., and Zhang, C. 2011. A Prospective Study of Prepregnancy Dietary Iron Intake and Risk for Gestational Diabetes Mellitus. Diabetes Care, vol. 34, no. 7, pp. 1557-1563 Ekoe, J., Rewers, M., Williams, R., and Zimmet, P. 2008. The Epidemiology of Diabetes Mellitus, 2nd Ed. New York: John Wiley and Sons. Escott-Stump, S. 2008. Nutrition and diagnosis-related care, 6th Ed. New York: Lippincott Williams & Wilkins. Fadl, H., Ostlund, K., Magnuson, A., and Hanson, U. 2010. Maternal and neonatal outcomes and time trends of gestational diabetes mellitus in Sweden from 1991 to 2003. Diabetic Medicine, vol. 27, no. 4, pp. 436-441 Feig, D., Zinman, B., Wang, X., and Hux, J. 2008. Risk of development of diabetes mellitus after diagnosis of gestational diabetes. CMAJ, vol. 179, no. 3, pp. 229-234 Gillman, M., Oakey, H., Baghurst, P., Volkmer, R., Robinson, J., and Franzcog, C. 2010. Effect of Treatment of Gestational Diabetes Mellitus on Obesity in the Next Generation. Diabetes Care, vol. 33, no. 5, pp. 964-968 Gunderson, E., Quesenberry, C., Jacobs, D., Feng, J., Lewis, C., and Sidney, S. 2010. Longitudinal Study of Prepregnancy Cardiometabolic Risk Factors and Subsequent Risk of Gestational Diabetes Mellitus: The CARDIA Study. American Journal of Epidemiology, vol. 172, no. 10, pp. 1131-1143 Kim, C., and Ferrara, A. 2010. Gestational Diabetes During and After Pregnancy. New York: Springer London: Jones & Bartlett Learning Nanda, S., Savvidou, M., Syngelaki, A., Akolekar, R., and Nicolaides, K. 2011. Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks. Prenatal Diagnosis, vol. 31, no. 2, pp. 135-141 Nnakwe, N. 2009. Community nutrition: Planning health promotion and disease prevention. Orshan, S. 2007. Maternity, newborn, and women's health nursing: Comprehensive care across the lifespan. London: Lippincott Williams & Wilkins. Poretsky, L. 2010. Principles of Diabetes Mellitus, 2nd Ed. London: Springer. Shah, B., Retnakaran, R., and Booth, G. 2008. Increased Risk of Cardiovascular Disease in Young Women Following Gestational Diabetes Mellitus. Diabetes Care, vol. 31, no. 8, pp. 1668-1669 Xiang, A., Kjos, S., Takayanagi, M., Trigo, E., and Buchanan, T. 2010. Detailed Physiological Characterization of the Development of Type 2 Diabetes in Hispanic Women With Prior Gestational Diabetes Mellitus. Diabetes, vol. 59, no. 10, pp. 2625-2630 Read More